Using wearables to enable cardiovascular-kidney-metabolic (CKM) risk profiling for personalized coaching

Google Research scientists Zeinab Esmaeilpour and Ahmed A. Metwally introduced an investigational machine learning framework that converts passive wearable signals — resting heart rate, daily step count, sleep duration and weekly Active Zone Minutes — into a "coachable health age" (CHA) and uses an action-conditioned world model to simulate personalized 12-week "what-if" habit trajectories for cardiovascular-kidney-metabolic (CKM) health. The CHA model is grounded in published epidemiological studies and reports a health age delta versus chronological age, where a negative delta such as −3 years indicates a physiological profile younger than calendar age. The framework, detailed in the arXiv papers "Personalized health coaching via coachable health age and counterfactual wearable trajectories" and "Cardiovascular-Kidney-Metabolic Health Age Using Wearables and Blood Biomarkers," also expands to routine blood biomarkers and was validated across three independent datasets.

November 11, 2026 Zeinab Esmaeilpour and Ahmed A. Metwally, Research Scientists, Google Research We introduce an investigational machine learning framework that translates passive wearable signals into a modifiable coachable health age, uses an action-conditioned world model to simulate personalized 12-week "what-if" habit trajectories, and expands to routine blood biomarkers to track early cardiovascular-kidney-metabolic CKM health. Heart disease, kidney conditions, and metabolic disorders like type 2 diabetes https://www.nature.com/articles/nrdp201519 aren't isolated, they form an interconnected web known as cardiovascular-kidney-metabolic CKM syndrome https://www.ahajournals.org/doi/10.1161/CIR.0000000000001186 . CKM progression often begins silently years before symptoms appear, shaped by everyday habits like movement, sleep, and cardiovascular activity. Catching these subtle shifts early offers the best chance to reverse the trend https://www.ahajournals.org/doi/10.1161/CIR.0000000000001453 . Yet while smartwatches easily track what we did yesterday, moving from passive tracking to meaningful action is much harder: How do my daily habits affect my CKM right now? And if I sleep 30 minutes longer or add a brisk walk to my routine, how much will my health improve over the next three months? Age is often seen as playing a part in answering these questions, but chronological age alone isn’t enough because two people born in the same year can have vastly different physiological health. While researchers have developed “ biological clocks https://www.nature.com/articles/s41591-023-02296-6 ” from blood tests or wearable sensors to estimate whether someone's body is aging faster or slower than their calendar age, most existing clocks aren't built for everyday coaching. Many are trained simply to estimate the birth year, latching onto traits you can't easily change, or act as black boxes that can't show how modifying a specific habit will shift your future health. In " Personalized health coaching via coachable health age and counterfactual wearable trajectories https://arxiv.org/pdf/2611.08568 ” and our companion study, “ Cardiovascular-Kidney-Metabolic Health Age Using Wearables and Blood Biomarkers https://arxiv.org/abs/2603.27787 ”, we introduce an investigational framework designed to close the loop between tracking a person’s health and actively improving it. The framework provides the following: To make a health metric useful for coaching, every input should be something that can be tracked continuously and influenced through daily choices. Rather than training a model to guess a person’s birth year, we grounded our coachable health age CHA model in published epidemiological studies linking everyday behaviors to cardio-metabolic disease risk. CHA integrates four core signals passively measured by consumer wearables — resting heart rate https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227709 , daily step count https://link.springer.com/article/10.1007/s40279-016-0663-1 , sleep duration https://www.nature.com/articles/s41591-024-03155-8 , and weekly Active Zone Minutes https://support.google.com/googlehealth/answer/14236509 exp — alongside optional markers like VO2max, https://link.springer.com/article/10.1007/s40279-021-01639-y sleep schedule regularity https://www.jacc.org/doi/10.1016/j.jacc.2019.12.054 , and insulin resistance https://www.nature.com/articles/s41586-026-10179-2 , when available. Because active people often have lower resting heart rates and better sleep, we mathematically adjust for overlap across these signals so each habit contributes its distinct share to your health age delta , the difference between your CHA and chronological age. A negative delta say, −3 years means an individual's physiological profile appears younger than their chronological age, while a positive delta signals accelerated aging. We validated this model across three independent cohorts: Knowing your health age tells you where you stand today. Turning that number into a personalized plan requires answering a "what-if" or counterfactual question: What will happen to my body over the next 12 weeks if I change specific habits, and which plan can I realistically stick with? Simply plugging new step or sleep targets into a static calculator misses how the body adapts as a connected system. When you consistently move more and sleep better over several weeks, your resting heart rate and heart rate variability gradually improve too, creating extra health benefits downstream. To capture these dynamics, we developed Sensor World Model SensorWM , an action-conditioned world model pre-trained on 2.28 million person-weeks of de-identified data from roughly 100,000 consenting Fitbit users. Given two weeks of baseline wearable data, SensorWM forecasts how 20 physiological markers will evolve over the next 12 weeks under different daily steps, sleep, and cardio routines, outperforming general-purpose time-series foundation models like TimesFM https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/ while preserving the clinical patterns associated with diabetes, hypertension, and insulin resistance. We paired SensorWM with a feasibility-constrained optimizer and a language model coaching agent to do three things: Does grounding a language model in world-model simulations actually produce better guidance than giving the exact same model a user's wearable history alone? We ran a blinded, head-to-head study with five certified lifestyle coaches and five board-certified primary care and lifestyle medicine physicians across 49 participant profiles spanning five behavioral personas 490 paired reviews total , alongside automated evaluations across the full WEAR-ME https://www.nature.com/articles/s41586-026-10179-2 cohort n = 2,780 . Because our health age model is modular, it can easily incorporate routine clinical lab tests when available. In our companion study https://arxiv.org/abs/2603.27787 , we expanded the framework to profile CKM syndrome across individual organ systems, pairing continuous wearables with occasional blood biomarkers that alone miss day to day lifestyle patterns. Early CKM decline rarely causes symptoms, and occasional clinic blood draws miss day-to-day lifestyle patterns. Evaluated in 819 WEAR-ME participants, our multimodal CKM health age model combines continuous wearable signals resting heart rate, steps, active zone minutes, and sleep with three routine blood biomarkers: cholesterol-to-HDL ratio cardiovascular , eGFR https://www.acpjournals.org/doi/10.7326/0003-4819-150-9-200905050-00006 kidney filtration , and HOMA-IR https://link.springer.com/article/10.1007/bf00280883 metabolic insulin resistance . By measuring how far each marker departs from healthy clinical thresholds a normalized deviance score , the CKM model revealed that multi-organ aging doesn't follow a single template: while metabolic and cardiovascular strain are often correlated, early kidney decline frequently appears independently. Moreover, the resulting CKM health age gap aligned closely with both objective clinical scores modified Life's Essential 8 r = −0.58; metabolic syndrome severity r = 0.65 and how healthy participants felt day to day. Together, these studies show how consumer wearables and machine learning can help shift personal health from looking in the rearview mirror to navigating the road ahead. While our evaluations were conducted offline using retrospective research cohorts, and these models are investigational research prototypes rather than clinical diagnostic tools, they lay the foundation for future prospective trials testing how interactive "what-if" coaching can help people build lasting habits and improve their long-term health. The research described here is joint work across Google Research and partnering teams. Contributors include Zeinab Esmaeilpour, Simon A. Lee, Menglian Zhou, Marius Guerard, Sohrab Sidhu, Alexandru Solot, Hamid Palangi, Pramod Rudrapatna, Ming-Zher Poh, Max A. Xu, Daniel McDuff, Dimitrios Antos, Yuzhe Yang, Jeffrey N. Chiang from UCLA , Anthony Z. Faranesh, Conor Heneghan, Lindsey Sunden, Mark Malhotra, Cathy Speed, Javier L. Prieto, Shwetak Patel, A. Ali Heydari, and Ahmed A. Metwally.